How should organisations define observability for non-deterministic systems?

What observability means when the system is non-deterministic

For non-deterministic systems, observability should be defined by whether the system is still producing the business outcome the organisation depends on, not whether each internal step looks technically successful. The key shift is from tracing every execution path to proving that important user journeys, service levels, and decision outcomes remain within acceptable bounds even when the internal path varies.

This matters because the same input can legitimately produce different outputs, different tool choices, or different execution sequences. In that setting, a clean log line or a returned 200 status can hide degraded quality, unsafe behaviour, or a failed outcome. NIST Cybersecurity Framework 2.0 is useful here because it reinforces outcome-oriented governance across identify, protect, detect, respond, and recover rather than treating monitoring as a narrow technical exercise.

Practically, the observability definition should name the outcomes that matter, the acceptable range of variation, and the signals that show whether variation is still safe. That gives teams a stable way to monitor systems whose internals may be probabilistic, adaptive, or context-dependent without pretending they are deterministic.

What to measure instead of only technical success indicators

The most useful observability signals are usually downstream from the internal mechanics: task completion rate, answer correctness, policy compliance, latency budgets, escalation frequency, user abandonment, revenue conversion, or error recovery quality. If a non-deterministic system can satisfy internal checks while still harming a user-facing or revenue-relevant condition, that condition must be observable in its own right.

This often means pairing traditional telemetry with domain-specific evidence. For example, an assistant might report that a tool call succeeded, but observability should still reveal whether the resulting action was the right one, whether the right data was used, and whether the user ended in a safe and useful state. OWASP Agentic AI Top 10 is relevant because it reflects how tool use, identity and privilege abuse, and cascaded failures can create misleading signals that simple execution monitoring will miss.

The best definitions also include negative evidence. A lack of errors is not enough if the system quietly produced the wrong outcome, failed to escalate, or made the wrong decision with high confidence. In non-deterministic environments, observability is as much about proving absence of harmful states as it is about confirming success.

How to set thresholds, alerts, and investigation criteria

Observability becomes actionable when the organisation defines thresholds around acceptable outcome variance, not just infrastructure health. That means deciding what deviation is tolerable, what signal indicates a quality regression, and when a pattern is severe enough to trigger investigation or rollback. The threshold should be tied to business harm, not to whether the system followed a perfectly repeatable path.

A useful rule is this: if the user or business impact can worsen without a traditional error, it belongs in monitoring and alerting. That includes silent accuracy drift, policy bypass, inconsistent decisions, partial completion, and repeated recovery behaviour that keeps the system technically alive but operationally unreliable. NIST SP 800-53 Rev 5 Security and Privacy Controls supports this style of thinking through auditability, system integrity, and monitoring controls that help teams establish whether the control environment is actually detecting material failure.

Investigation criteria should also distinguish between noise and meaningful instability. A few benign variations may be expected, but repeated drift in the same business outcome, or a sudden increase in human intervention, is often the earliest reliable sign that the system is no longer observable in a useful way.

Practitioner Guidance

What to prioritise: Define the top 5 to 10 outcomes that would matter if the system behaved “successfully” but still caused harm, and make those the primary observability targets. If a metric cannot support a decision to alert, investigate, or suppress a release, it is probably not a real observability signal for this class of system.

What to verify: Check that each critical outcome has both a positive signal and a failure signal. If you only measure throughput, latency, or tool execution success, you are likely blind to silent quality regression. The control is working only when operators can explain why the system is healthy in business terms, not just in infrastructure terms.

Decision rule: If the system can produce materially bad results while remaining technically “up”, treat outcome monitoring as mandatory and use internal telemetry only as supporting evidence. For non-deterministic systems, a stable runtime is not the same thing as a trustworthy service.

Practitioner takeaway: The observability target is the decision or outcome the organisation cares about, because that is what will fail first when a non-deterministic system degrades.

Related resources from NHI Mgmt Group

• How should security teams red team non-deterministic AI systems?
• What do organisations get wrong about observability in agentic systems?
• Why do non-deterministic AI systems create governance risk in security pipelines?
• What breaks when organisations do not inspect non-visible content in emails, PDFs, and web pages before AI systems process them?